How to Fix 'prompt template error in production' in LlamaIndex (TypeScript)

What the error means

prompt template error in production usually means LlamaIndex failed while rendering or validating a prompt template before sending it to the LLM. In TypeScript, this shows up most often when your PromptTemplate variables do not match the arguments you pass at runtime, or when a custom prompt is wired into the wrong component.

You’ll typically hit it during query execution, chat engine calls, retriever synthesis, or when overriding built-in prompts on classes like VectorStoreIndex, RetrieverQueryEngine, or CondenseQuestionChatEngine.

The Most Common Cause

The #1 cause is a variable name mismatch between the template and the values you pass in.

LlamaIndex’s prompt classes are strict. If your template says {context_str} but you pass {context} or forget one variable entirely, you’ll get errors like:

• •Error: Missing value for input variable 'context_str'
• •PromptTemplateError: Invalid prompt template
• •Cannot render prompt template

Broken vs fixed

// BROKEN
import { PromptTemplate } from "@llamaindex/core/prompts";

const prompt = new PromptTemplate({
  template: "Answer using this context:\n{context_str}\n\nQuestion: {query_str}",
});

const rendered = await prompt.format({
  context: "Customer data here", // wrong key
  query: "What is the policy limit?", // wrong key
});

// FIXED
import { PromptTemplate } from "@llamaindex/core/prompts";

const prompt = new PromptTemplate({
  template: "Answer using this context:\n{context_str}\n\nQuestion: {query_str}",
});

const rendered = await prompt.format({
  context_str: "Customer data here",
  query_str: "What is the policy limit?",
});

If you’re using higher-level APIs, the same rule applies. For example, if you override a response synthesizer prompt used by RetrieverQueryEngine, make sure the placeholder names match what that synthesizer actually injects.

Other Possible Causes

1) You passed a plain string where LlamaIndex expects a PromptTemplate

This happens when developers assign raw strings into config objects that expect prompt instances.

// BROKEN
const queryEngine = index.asQueryEngine({
  textQaPrompt: "Use only the provided context.", // wrong type
});

// FIXED
import { PromptTemplate } from "@llamaindex/core/prompts";

const queryEngine = index.asQueryEngine({
  textQaPrompt: new PromptTemplate({
    template: "Use only the provided context.\n\nContext:\n{context_str}\n\nQuestion:\n{query_str}",
  }),
});

2) Your custom prompt uses unsupported placeholders

LlamaIndex templates are not free-form. If you invent a variable name that no upstream component supplies, rendering fails.

// BROKEN
const prompt = new PromptTemplate({
  template: "Context:\n{docs}\n\nQuestion:\n{question}",
});

If your chain expects context_str and query_str, those names must be used exactly.

// FIXED
const prompt = new PromptTemplate({
  template: "Context:\n{context_str}\n\nQuestion:\n{query_str}",
});

3) You overrode one side of a paired prompt but not the other

This shows up in chat flows. For example, a condense step may expect one schema and the answer step another. If you replace only one prompt and keep incompatible placeholders elsewhere, you’ll get runtime failures inside CondenseQuestionChatEngine.

// BROKEN
chatEngine.updatePrompts({
  "condense_question_prompt": new PromptTemplate({
    template: "Rewrite this user message:\n{message}",
  }),
});

If that engine internally provides question instead of message, rendering breaks.

// FIXED
chatEngine.updatePrompts({
  "condense_question_prompt": new PromptTemplate({
    template: "Rewrite this user message:\n{question}",
  }),
});

4) Version mismatch between packages

In TypeScript projects, mismatched LlamaIndex package versions can produce weird prompt behavior because APIs move between releases.

Check for combinations like:

• •@llamaindex/core
• •@llamaindex/openai
• •framework-specific integration packages

A broken install often looks like this in practice:

{
  "dependencies": {
    "@llamaindex/core": "^0.x",
    "@llamaindex/openai": "^0.y"
  }
}

If those versions are out of sync, update them together and reinstall cleanly.

How to Debug It

1. •

   Print the exact error text

   • •Look for messages like:
     • •Missing value for input variable 'query_str'
     • •Invalid prompt template
     • •PromptTemplateError
   • •The missing variable name usually tells you what’s broken immediately.
2. •

   Inspect every placeholder in your template

   • •Count them manually.
   • •Compare them against the object passed to .format(...) or whatever wrapper your engine uses.
   • •If your template has {context_str} and {query_str}, both must be present.
3. •

   Remove custom prompts temporarily

   • •Revert to default prompts on VectorStoreIndex, RetrieverQueryEngine, or chat engines.
   • •If the error disappears, your override is the problem.
   • •Re-add custom prompts one by one until it breaks again.
4. •

   Log rendered inputs before calling LlamaIndex

   • •In TypeScript, print the payload just before formatting or query execution.
   • •Verify keys are exactly right:

     console.log({ context_str, query_str });
     

   • •This catches undefined values fast.

Prevention

• •Use typed helper functions around your prompts so placeholder names stay consistent across your codebase.
• •Keep custom prompts close to where they’re used, especially for engines like RetrieverQueryEngine and chat wrappers.
• •Pin compatible LlamaIndex package versions together and upgrade them as a unit.

If you want fewer production surprises, treat prompts like function signatures. The variable names are part of the contract.

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By Cyprian Aarons, AI Consultant at Topiax.